Projected squeezing of the wintertime North-Atlantic jet

We use an ensemble of 36 historical and RCP8.5 simulations from CMIP5, over the 1961–2095 period. The list of models included is given in table S1 available at stacks.iop.org/ERL/13/074016/mmedia (we use one realization per model). Details on the CMIP5 protocol can be found in Taylor et al (2012). All model output is interpolated to a horizontal 1.9 × 2.5° grid and 17 vertical levels. We also use 40 ensemble members from the Community Earth System Model Large Ensemble (CESM-LENS, Kay et al 2015). Each ensemble member consists of a different realization of a coupled ocean-atmosphere 1920–2100 simulation, forced by historical then RCP8.5 radiative forcing. The ensemble members only differ by perturbations in atmospheric initial conditions, giving an estimate of the importance of internal variability in the climate change response.

A variety of metrics are used to characterize the mid-latitude atmospheric dynamics in the model outputs. They are defined as follows:

• The zonal index (ZON) measures the strength of the average zonal flow in a given longitudinal sector. It is defined as the difference between 500 hPa geopotential height (Z500) in the high-latitudes (60–90°N) and in the mid-latitudes (20°–50°N).
• The sinuosity metric (SIN) is similar to the one described in Cattiaux et al (2016). For every day, the average Z500 between 30°N and 70°N is defined as the reference isohypse, the length of which is measured and divided by the length of the 50°N latitude circle. Sinuosity is therefore a ratio greater than 1 (1 representing a perfectly zonal flow), that measures the waviness of the mid-latitude (50°N) flow over a given longitudinal sector.
• Blocking events in mid-latitudes are identified using the 1-D blocking index (BLO) from Tibaldi and Molteni (1990), i.e. by identifying reversals in the Z500 meridional gradient that persist for at least 5 days.
• The jet width index (JWI) is derived from the average 30–70°N daily Z500 contours. For each month, the daily contours are zonally-averaged over the longitudinal sector, and a Gaussian fit is applied to the latitudinal distribution of the contours. The jet width is then defined as the latitudinal band that includes 95% of the daily contours (in ° latitude).

Several large-scale signals of climate change are defined as follows:

• UTW is defined as the zonal mean temperature change in the (20°S/20°N; 400–150 hPa) domain.
• AA is defined as the zonal mean temperature change in the (60°N/90°N ; 1000–700 hPa) domain.
• The ratio UTW divided by AA is referred to as the RUTAW ('ratio between upper-troposphere tropical and Arctic warming') index.
• The polar stratospheric temperature (PST) is defined as the zonal mean temperature in the (70°N/90°N; 250–30 hPa) domain.
• UPTG is the upper-troposphere temperature gradient, computed as the difference between the zonal temperature in (20°S/20°N; 400–150 hPa) and (70°N/90°N; 400–150 hPa). LOTG is the lower-troposphere temperature gradient, computed as the difference between the zonal-mean temperature in (20°S/20°N; 1000–700 hPa) and (70°N/90°N; 1000–700 hPa).
• The Hadley cell width (HCW) is defined following Stachnik and Schumacher (2011). HCW is the distance (in degree-latitude) between the first latitude where the 700–400 hPa average value of the meridional streamfunction equals zero in each hemisphere.
• SSTG45 is the average meridional gradient of SST in the (45°N/55°N; 80°W/30°W) domain.
• Change in cold extreme temperature is assessed defining cold days at each grid point, based on the 1976–2005 distribution of minimum daily temperature of each model. A day that has its minimum temperature below the 10th percentile (Q10) of the present-day distribution is defined as a cold day, and the deviation from Q10 as the cold day anomaly (always negative). Then we sum the cold day anomalies for each month to construct a cold day intensity (CDI) index, expressed in degree-day. This metric allows us to account for changes in both the frequency and intensity of cold days.

Late 21st century changes are expressed as the difference between 2066–2095 and 1976–2005 in CMIP5, 1981–2010 and 2071–2100 in CESM-LENS. All analyses are October–March (ONDJFM) seasonal averages. The statistical significance of the anomalies is assessed using a two-tailed Student t-test.

Figure 1 depicts the future minus present-day changes in zonal wind, at 700 hPa (U700, figures 1(a) and (d)) in zonal average (figures 1(b) and (e)), and as a function of time for U700 (figures 1(c) and (f)), over the North Atlantic sector (40°W–50°E). In both CMIP5 and CESM-LENS, we identify a tripole of zonal wind anomalies in the lower troposphere, with decreased westerlies on both flanks of the jet, and increased westerlies at its core (figures 1(a), (b), (d) and (e)). This signal represents a narrowing of the eddy-driven jet, as previously discussed in Peings et al (2017) for CESM-LENS. It is only found in winter (that we define as the 'extended-winter' October–March season) and it is more pronounced in OND in CMIP5, and JFM in CESM-LENS (not shown). The good agreement across a large ensemble of models (CMIP5) and different realizations of a single model (CESM-LENS) proves that it is a robust feature of RCP8.5 projections (note that the statistical significance is generally higher for CESM-LENS than for CMIP5, due to larger variance in CMIP5 that includes model uncertainty). The narrowing of the eddy-driven jet emerges significantly around 2070, starting with decreased winds on the poleward flank of the jet followed by a reduction on the equatorward flank (figures 1(c) and (f)). The narrowing signal over the North Atlantic dominates the zonal average but it is very sector-dependent (figure S1). The North Pacific sector also exhibits a narrowing of the jet (figure S1), although with less amplitude, while the jet tends to shift southwards over North America (Vavrus et al 2016).

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In order to further characterize the changes in the mid-latitude circulation, we use the ZON, SIN, BLO and JWI indices, that respectively measure the strength, waviness, reversal and width of the zonal westerly flow at 50°N over the North Atlantic. Their temporal evolution in CMIP5 and CESM-LENS is shown in figure 2, along with their future minus present-day mean change. On average, at the end of the 21st century, the zonal index shows a small increase in both ensembles of simulations (figure 2(a)), while sinuosity and blocking decrease by about 10% and 20%, respectively (figures 2(b) and (c)). This average response is opposite to the hypothesized effect of Arctic Amplification (i.e. increased waviness/blocking with weaker westerlies in mid-latitudes, Francis and Vavrus 2012), consistent with results from numerical sensitivity studies (Oudar et al 2017, McCusker et al 2017). The significant decrease in JWI (figure 2(d)) reflects the narrowing of the eddy-driven jet identified in figure 1. Our interpretation of these results is that a narrower (and stronger) jet allows for less meandering around its mean position, leading to less waviness and blocking events in the circulation (Barnes and Polvani 2013).

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The circulation changes described in the previous section exhibit a large spread, both among CMIP5 GCMs and CESM-LENS ensemble members (cf envelopes and error bars in figure 2). In this section we aim to understand which are the main drivers that cause this spread. In particular, models present a range of sensitivities in terms of future temperature changes. Figure 3(a) show the change in zonal mean temperature in CMIP5 (it is very similar in CESM-LENS). Apart from the well-known tropospheric warming/ stratospheric cooling response, two signals stand out in the temperature anomaly pattern: the strong UTW and the surface AA signal. As CMIP5 combines both model uncertainty and internal variability, it presents a larger spread than CESM-LENS (internal variability alone) in UTW and AA (figures 3(b) and (c)). CMIP5 is therefore more helpful than CESM-LENS to highlight the role of UTW and AA in shaping the response of the mid-latitude atmospheric circulation. However, CESM-LENS gives us an estimate of uncertainties due to internal variability, that we use in figure 4 to estimate the error that is made when using a single realization of a model.

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A large combination of inter-model regression analyses between the changes in dynamical indices (ZON, SIN, BLO and JWI) and in climate change indices listed in section 2.3 have been computed. In addition to UTW and AA, we compute regressions using the global mean surface temperature (TGLO), the upper- and lower-troposphere latitudinal temperature gradients (UPTG and LOTG, respectively) and the PST (their respective timeseries/mean changes are shown in figure S2). The spread in TGLO (figures S2(a)) represents the diversity in climate sensitivity among the models (it is, as expected, very small in CESM-LENS). UPTG increases in the model due to UTW, while LOTG decreases with AA (figures S2(b) and (c)). Future changes in PST are more model-dependent and internally-driven than UTW and AA (cf large spread in figure S2(d)), as expected from high internal variability in the stratosphere (Manzini et al 2014). Since UTW and AA are both correlated with TGLO, it is difficult to isolate their respective influence on the dynamical changes. For this reason, we use the RUTAW index, simply defined as the ratio between UTW and AA⁶. Unlike UTW and AA, RUTAW does not exhibit a significant trend over the 21st century in CMIP5, with a large spread between the models (figure 3(d)). Therefore, RUTAW allows us to efficiently separate the models that have a strong UTW relative to AA (positive RUTAW), from the models that exhibit a dominant AA versus UTW (negative RUTAW) (see figure S3). We also include an index of Hadley cell expansion (Hadley Cell Width, HCW), in order to assess whether this robust climate change signal in both CMIP5 and CESM-LENS (figures S4(a) and (b)) is a driver of the mid-latitude dynamical changes. A last index is the meridional gradient of sea surface temperature (SST) at 45°N in the North Atlantic (SSTG45, figures S4(c) and (d)), SST gradients in the Gulf Stream region being a potential driver of baroclinicity hence of the downstream atmospheric flow (O'Reilly et al 2017).

Correlation coefficients of the standard regression analyses are summarized in table 1, along with the variance explained by the best 2 predictor linear regression model used to predict each dynamical index. Note that all the indices but HCW are zonally-averaged over the North Atlantic sector (similar results using the NH zonally-averaged indices are given in table S2). The scatterplots corresponding to the highest correlation found for each dynamical index are shown in figure 4, using an error-in-variable (EIV) regression that uses the CESM-LENS spread to estimate uncertainties due to internal variability alone (see appendix for description of the method). The highest correlation is found between RUTAW and ZON (R_EIV = 0.84 with a 95% confidence interval of 0.59/0.93, figure 4(c)). RUTAW explains significantly more variance in ZON than UTW and AA alone (table 1). The positive correlation indicates that GCMs with greater warming in the tropics relative to the Arctic have positive ZON anomalies over the North Atlantic (i.e. positive North Atlantic Oscillation), and conversely. When coupled with PST, the multiple regression model RUTAW+PST explains 66% of the variance in ZON. RUTAW also exhibits the highest correlation with JWI (figure 4(d)), while BLO and SIN are more strongly correlated with UPTG (figure 4(a)–(b)). SSTG45 also emerges as a good predictor of BLO, SIN and JWI (table 1), although a causal relationship is difficult to assess because of the two-way relationship between atmospheric and SST changes. HCW is negatively correlated with SIN, i.e. models with a stronger HC expansion tend to be the ones with less waviness in the mid-latitude flow. Corresponding correlations using the CESM-LENS spread (grey dots in figure 4, and table S3) are smaller, especially with RUTAW, UTW and AA, as expected due to the absence of model uncertainty.

Overall, these results suggest that the competition between AA and UTW (represented by RUTAW) can explain a significant part of the multi-model spread in changes of the zonal westerly flow over the North Atlantic (ZON and JWI). In general, GCMs with dominant tropical warming project a stronger and narrower westerly flow, while GCMs with larger Arctic warming respond the opposite way. Synoptic metrics (BLO and SIN) appear to be more sensitive to changes in the upper-troposphere temperature gradient, with less influence of Arctic Amplification. Consistent with previous studies (Manzini et al 2014, Zappa and Shepherd 2017, Peings et al 2017), we also find a connection with the polar stratospheric temperature. SIN and JWI decrease more strongly in models/members with a cooler polar stratosphere (PST) at the end of the 21st century. Whether the stratosphere drives or responds to the tropospheric changes is not addressed in this study, but this is a robust link both in CMIP5 (table 1) and CESM-LENS (table S3).

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In order to further highlight the respective influence of UTW and AA on the mid-latitude atmospheric response, we composite the CMIP5 models based on their future vs present-day change in RUTAW index. The six models with the highest change in RUTAW ('UTW effect') are compared to the six models with the lowest change in RUTAW ('AA effect'). The corresponding zonal temperature anomalies over the North Atlantic are shown in figures S3(a). Note that such an analysis using CESM-LENS is not effective, due to the lack of variability in RUTAW to efficiently separate UTW from AA (figures S3(b)). The UTW-effect models project a reinforcement and slight poleward shift of the eddy-driven jet (figures 5(a) and (d)), a signal that is absent in the AA-effect models, in which the main signal is a significant reduction in the westerlies on the poleward flank of the jet (figures 5(b) and (e)). Although reduced, AA is still present in the high-RUTAW composite, as is UTW in the low-RUTAW composite (figure S3(a)), but their difference maximize the signal and reveal their competing influences (figures 5(c) and (f)). In the absence of AA, the eddy-driven jet would migrate poleward over the eastern Atlantic and Europe, as expected from an increase in upper-level meridional temperature gradient (figure 5(f)). However, AA counteracts this effect by reducing the westerlies on the poleward flank of the jet, in agreement with Zappa et al (2018), and recent numerical sensitivity studies (Deser et al 2015, Blackport and Kushner 2017, Oudar et al 2017, McCusker et al 2017). The two effects combine in producing a narrowing and decreased waviness/blocking of the flow.

Figures 5(g)–(i) shows the changes in 2 meter temperature (T2M). Not surprisingly, the temperature strongly increases almost everywhere, especially over continental and high-latitude areas. However, models with a stronger UTW-effect exhibit less warming along the path of the reinforced jet, from the south of Greenland to the Barents Sea (figure 5(i)). This cooling is associated with cooler SST in the subpolar regions (figures S5(d)–(f)). Such signal may be partly driven by the jet reinforcement and increased wind stress and turbulent heat flux exchanges at the surface. However, oceanic processes also play a role. In particular, a weaker Atlantic meridional overturning circulation (AMOC), a response projected by CMIP5 models at the end of the 21st century and beyond (Cheng et al 2013), is associated with decreased deep convection and cooler SST in the subpolar North Atlantic (Sgubin et al 2017). In particular, cold SST anomalies in the subpolar gyre (or 'North Atlantic warming hole', Drijfhout et al 2012) is a marker of a weaker AMOC, and we find it is more pronounced in UTW-effect models (figures S5(d)–(f)). Moreover, the UTW-effect models show less decrease in Arctic sea ice (figures S5(c)) and warmer SST in the South Atlantic (figures S5(f)), two signals that are consistent with a reduced AMOC and decreased heat transport in high-latitudes. A decreased meridional heat transport is also consistent with a warmer tropical atmosphere, i.e. a higher RUTAW, since more energy accumulates in the tropics. The significant correlation between the intensity of the North Atlantic warming hole and RUTAW (figure S6) supports this interpretation, although further analyzes of oceanic variables will be needed to explore this question in more detail. The role of the AMOC and ocean dynamics in the Arctic-versus-tropics tug-of-war is an interesting prospect for future studies.

Finally, we show how the different jet responses affect extreme temperature over Europe. From a dynamical perspective, with a stronger westerly flow, one might expect even less cold extremes in winter in the dominant UTW-effect models, due to reduced north-south excursions of the jet and associated cold air outbreaks. Actually, the intensity of cold days (CDI, see definition in section 2.3) decreases less in the UTW-effect models than in the AA-effect ones, especially over Western Europe (figures 5(j)–(l), Łpositive values represents a decrease in CDI). This can be explained by thermodynamical changes in high-latitudes, that counteract changes in dynamics (Ayarzagüena and Screen 2016). Although a stronger westerly flow allows for less meandering of the jet, the reduced surface warming in high-latitudes, promoted by cooler SST in the subpolar Atlantic (figures S5(f)) and less sea ice loss in the Barents-Kara sea (figures S5(c)), results in cooler air advection from the north-east during cold air outbreaks than in models with a dominant AA-effect. Again, it will be interesting to quantify the respective role of atmosphere and ocean dynamics processes (i.e. AMOC) in dedicated sensitivity GCM experiments.